The document discusses the challenges of network optimization in a WAN-cloud environment, highlighting issues like over-provisioning bandwidth and traditional network limitations that hinder performance. It emphasizes the need for dynamic bandwidth allocation and application-aware networks to address congestion and improve overall quality of experience. The role of advanced analytics and SD-WAN solutions like Talari is underscored for enhancing network efficiency and adaptability.

1. Challenges of Network
Optimization in a
WAN-Cloud World
@AtchisonFrazer
April 2017

2. 2
Large global manufacturer (automotive):
Core challenge: Overinvesting in
‘Bandwidth Capacity Planning’
“Our network does not represent a steady
state environment. It is constantly having
new demands placed on it; each of these
demands require more bandwidth to be
always available. If that bandwidth is not
available, the network must be able to buffer
traffic sufficiently until the congestion clears.”
“Make My Network Flow Like Air”

3. 3
• Pause + Rate Limit
• Buffering + Random Dropping
• Congestion Diagnostics – L3
L3L2
Network Disruption
15%
Performance
15%
Performance
Software Analysis
Congestion 15%
Performance
Packet Drop/Loss
15%
Performance
Buffering
CONGESTION MITIGATION
• Random path routing
• Rogue broadcasting (NIC)
• Last Mile edge penalty
CORE NETWORKING
Traditional L2-L3 Networking Limitations

4. 4
Conventional Assumptions: How to Avoid . . .
Premium
Vendor Lock-in
Installed
Base
Lifecycle
Churn
Optimization
Software &
Services
Layers
Without visibility to QoE application behavior and associated packet bandwidth, organizations
are either likely to over-provision hardware assets or connectivity connections, leaving too
little headroom for conventional Cloud-WAN networks to scale with end-user demands.

5. 5
High Touch Engineer
Core Infrastructure (L2/L3)
Network Analytics Software
Break/Fix Maintenance with Smart Diagnostics
Professional Consulting
Application Visibility
Architecture Advisory
Trap #1: Legacy Equipment Optimization Tax

6. 6
Server A
Server B
Server C
Server D
Server E
Server F
S0
L0 L3
S1 S2 S3
L1 L2 L4 Ln
• Server A is sending a lot of frames to server D
• Server B becomes active sending frames to server E; server C becomes active sending frames to server F
• Links between node L0 and S0 and S0 and L3 congest; WAN Last Mile penalized
Trap #2: Traditional Static Network Orthodoxy

7. 7
Congestion
15%
Performance
15%
Performance
15%
Performance
15%
Performance
T1
MPLS
• Random path routing
• Rogue broadcasting (NIC)
• Last Mile edge penalty
CORE NETWORKING
Trap #3: Over-Provisioning Hardware & Bandwidth

8. 8
BRANCHDATACENTER
MPLS
SAAS
Internet
Link quality measured for EVERY packet of EVERY application continually in EACH direction?
Allocates available bandwidth dynamically end-to-end based on predictive analytics
for degrading conditions and failsafe maneuvers for congestion avoidance?
MAC
IP
TCP/UDP
DATA
Packet
Millisecond
measurement of
latency, loss, jitter
probabilities?
What happens when packets reach the BRANCH?
Typical WAN Branch Network Topology
App-aware: Real-Time?
Interactive? Bulk?
Performance Control?
Cloud-aware: Resilient
Connection? Disaster
Recovery? QoS Metrics?
QoS b/w slack
for business
critical apps with
QoE metrics?

9. 9
Talari Networks Next Gen SD-WAN: Cloud/App-Aware Optimization
What happens in a Talari powered BRANCH?

10. 10
@AtchisonFrazer
Talari.com